The South

This study deals with statistics regarding the area in the continental United States generally known as “The South.” THe region, which is otherwise known as “The Bible Belt”, generally includes the states Arkansas, Mississippi, Alabama, Tennessee, Georgia, South Carolina, Kentucky and North Carolina. However, there are states and/or regions that are included or excluded based on which factors are being used to describe this region. For the sake of this study, only these states - as shown below - will be studied, however.

In the South, there continues to be many disparities - especially those related to race and economical status - that exists today. The purpose of this study is to explore specific disarities among those living in the South.

Being from Bartlett, TN, I was drawn to this topic.

Background Information

Racial inequality in the United States refers to social advantages and disparities that affect different races within the United States. These inequities may be manifested in the distribution of wealth, power, and life opportunities afforded to people based on their race or ethnicity, both historic and modern. These can also be seen as a result of historic oppression, inequality of inheritance, or overall prejudice, especially against minority groups.

Since 1980, racial and ethnic disparities in poverty in the U.S. have remained largely unchanged, resulting in what the researchers characterize as “two Americas.”

Whereas blacks, Hispanics and Native Americans are more likely to experience the high-poverty America, Asians and whites are more likely to experience the low-poverty America.

The South and Race

There are geographic differences between where white Americans live and where those of minority races live. Despite the many

The graph above displays the relationship between concentration of white populatuib and of minority population. As shown above, there is a negative correlation between white populations and those of other races. This correlation is linear, which is reasonable due to all races being accounted for.

As a result, I solely compared white populations and black populations to see if the linear relationship remained.

Note: The graph below is not the third graph, but merely a supplement of the first graph.

Again, there is a correlation between the white population and those of minority populations - specifically black populations, in this case. However, the relationship is not as linear as when all the minority races are accounted for. From the data given, it appears that size of the population has no significant influence to this correlation, as the outliers appear to be of various sizes.

There are several possible causes for this correlation. This relationship may be due to de facto or de jure segregation. Other causes may be due to dispairites in income or chances of receiving reasonable mortages. Because this data was not provided on the census, however, these possible correlations could not be studied for this project.

The South and Racial Segregation

Nonetheless, a possible explanation that could be examined by examining the relationship between net wealth and race. For most Americans, household wealth is closely tied to home equity. Home ownership helps families to accumulate wealth and to take advantage of sizable tax savings. Also, home ownership is able to be passed to later generations, allowing their children to generate further wealth. As shown by other studies, there are significant disparities in homeownership between blacks/minorities and whites.

As a result, I decided to explore the relationship between the average annual income and the percentage of white Americans residing in a certain area. In doing so, I was able to examine the Southern regions that are the most racially segregated, as well as the correlating income of these regions.

As can be seen above, many of the more segregated regions appear to be “clumped” together. The areas that are composed of mostly white people appear to be the northern part of the Bible Belt, namely northern Arkansas, Tennesseee, and Kentucky. However, it appeared that there was no discernable correlation between average income and higher percentage of white populations. This may be due to the fact that there is no significant correlation or due to the many points that are graphed, which makes it any possible correlation hard to see for this particular graph type.

However, as shown on the graph, greater average income is concentrated in prominent cities, such as Memphis or Birmingham. Because this graph does not efficently explain the relationship between wealth and race, this relationship will be studied later on in the project.

For comparison sake, a zoomed out version of the entire continental United States is shown below. Similar to the data above, the figure displays the percentage of white Americans in certain regions, as well as the average income.

Note: The graph of the entire continental United States is not the third graph, but merely a supplement of the second graph.

Interestingly enough, there is a larger concentration of minority races in the South, in comparison to the rest of the continental United States. However, there are places outside of the “Bible Belt” region that do contain larger concentrations of minority races.

According to the United States Burea, as of 2017, the 10 states with the largest minority populations were “New York (3.3 million), Florida (3.2 million), Texas (3.2 million), Georgia (3.1 million), California (2.7 million), North Carolina (2.2 million), Illinois (2.0 million), Maryland (1.8 million), Virginia (1.7 million) and Ohio (1.5 million)” (US Census Bureau, 2018).

The South and Income

To explore the relationship between race and income disparities in the South, I decided to investigate if there was a correlation between white populations and the regions containing the top 5% of income.

As one can see above, the greatest income is possessed by regions with the highest percentage of white populations and lowest percentage of minority populations. However, because this graph is examining the top 5% of income, the relationship may be different for the average income and race.

Future Implications